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Design of robust nonsquare constrained model‐predictive control
Author(s) -
Sarimveis Haralambos,
Genceli Hasmet,
Nikolaou Michael
Publication year - 1996
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690420919
Subject(s) - model predictive control , control theory (sociology) , mathematics , mathematical optimization , nonlinear system , control variable , norm (philosophy) , control (management) , computer science , statistics , physics , quantum mechanics , artificial intelligence , political science , law
A model‐predictive control (MPC) design methodology for processes with more manipulated inputs than outputs is developed. Essential features of the proposed approach are the following: the on‐line optimization minimizes an objective function based on the l 2 norm; an end‐condition equation is utilized; model uncertainty is considered as upper and lower bounds on the pulse‐response‐model coefficients; hard constraints on the input and move‐size variables and soft constraints on the output variables are posed. A major difference between square and nonsquare MPC is that in the former the end‐condition can be used directly, while in the latter a nonlinear programming problem needs to be solved during the design phase to select values for the input move suppression coefficients. This technique is illustrated through a number of simulations and application to a real industrial process.